Senior Data Engineer
$92,300–$166,850 year
On-site · Alexandria, Virginia, United States
Job Summary
As a Senior Data Engineer, you will design, build, and maintain data pipelines and architectures to support data ingestion, transformation, integration, storage, and dissemination. You'll apply software engineering and ETL principles to ensure data accuracy, quality, consistency, and scalability, and integrate COTS and customer-developed tools within existing data frameworks. Collaboration with DataOps teams is key for preparing, automating, and optimizing data workflows for real-time analytics. You will also implement and enforce data security policies, monitor data quality, and provide support for enterprise data products. Candidates must have an active Top Secret (TS) clearance, relevant educational background, and a minimum of 8 years in data engineering.
Required Qualifications
- Active Top Secret (TS) clearance with SCI eligibility
- Bachelor’s degree in Computer Science, Data Science, Engineering, Information Systems, or related technical discipline and 8–12 years of relevant experience OR Master’s degree in a related field and 6–10 years of relevant experience
- Minimum of 8 years of experience in data engineering or related roles
- Proven experience in designing and implementing data pipelines and architectures
- Experience with data quality monitoring tools and processes
- Excellent communication and interpersonal skills
- Experience developing and maintaining enterprise-scale data pipelines in cloud environments (AWS, Azure, or GCP)
- Experience implementing ETL/ELT processes and data orchestration frameworks
- Strong knowledge of ETL processes and data integration techniques
- Experience working with structured and unstructured data sources, including APIs, streaming platforms, and relational/non-relational databases
- Experience integrating data pipelines into DevSecOps CI/CD environments
Desired Qualifications
- Experience with cloud-based data solutions and architectures
- Familiarity with data governance frameworks and best practices
- Experience operating within SAFe or large-scale Agile frameworks supporting enterprise systems
- Experience supporting data platforms across NIPRNet, SIPRNet, and JWICS environments
- Experience implementing data governance controls, lineage tracking, and metadata management frameworks
- Experience supporting AI/ML data preparation and feature engineering workflows
- Knowledge of machine learning and AI principles
- Experience working with containerized environments (e.g., Docker, Kubernetes)
- Relevant cloud or data engineering certifications (e.g., AWS Data Analytics, Azure Data Engineer, or equivalent)
- Certifications in data engineering or related technologies (e.g., AWS Certified Data Analytics, Google Cloud Professional Data Engineer)
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